International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 15, No. 5, 8 Oct. 2025
Cover page and Table of Contents: PDF (size: 665KB)
Author(s)
Index Terms
IoT, DoS, DDoS, CNN GRU
Abstract
The growing adoption of Internet of Things (IoT) devices has amplified the need for robust security mechanisms, particularly against Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper proposes a deep learning-based detection system using a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN-GRU) model to effectively capture both spatial and temporal patterns of malicious activity. The CICDDoS2019 dataset is employed for training and evaluation, with preprocessing steps including Boruta-based feature selection and data rebalancing using SMOTE. A user-friendly GUI developed in Python (Tkinter) facilitates real-time input and prediction. The proposed model, Cyber Guard, demonstrates high accuracy and efficiency, offering a practical solution for IoT attack detection and future deployment.
Cite This Paper
Jinsu Anna John, Raj Kumar T., Shilpa Harrison, "Cyber Guard: Detecting DoS and DDoS Attack", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.15, No.5, pp. 57-67, 2025. DOI:10.5815/ijwmt.2025.05.05
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